Skip to main content
SpringerLink
Log in

IR Pedestrian Detection for Advanced Driver Assistance Systems

  • Conference paper
Book cover Pattern Recognition (DAGM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2781))

Included in the following conference series:

Abstract

This paper describes a system for pedestrian detection in infrared images implemented and tested on an experimental vehicle. A specific stabilization procedure is applied after image acquisition and before processing to cope with vehicle movements affecting the camera calibration. The localization of pedestrians is based on the search for warm symmetrical objects with specific size and aspect ratio. A set of filters is used to reduce false detections. The final validation process relies on the human shape’s morphological characteristics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Nanda, H., Davis, L.: Probabilistic Template Based Pedestrian Detection in Infrared Videos. In: Procs. IEEE Intelligent Vehicles Symposium 2002 (June 2002)

    Google Scholar 

  2. Xu, F., Fujimura, K.: Pedestrian Detection and Tracking with Night Vision. In: Procs. IEEE Intelligent Vehicles Symposium (June 2002)

    Google Scholar 

  3. Guilloux, Y.L., Lonnoy, J.: PAROTO Project: The Benefit of Infrared Imagery for Obstacle Avoidance. In: Procs. IEEE Intelligent Vehicles Symposium 2002 (June 2002)

    Google Scholar 

  4. Tsuji, T., Hattori, H., Watanabe, M., Nagaoka, N.: Development of Night-vision System. IEEE Trans. on Intelligent Transportation Systems 3, 203–209 (2002)

    Article  Google Scholar 

  5. Bertozzi, M., Broggi, A., Fascioli, A., Sechi, M.: Shape-based Pedestrian Detection. In: Procs. IEEE Intelligent Vehicles Symposium 2000, October 2000, pp. 215–220 (2000)

    Google Scholar 

  6. Gavrila, D.M., Geibel, J.: Shape-Based Pedestrian Detection and Tracking. In: Procs. IEEE Intelligent Vehicles Symposium 2002 (June 2002)

    Google Scholar 

  7. Curio, C., Edelbrunner, J., Kalinke, T., Tzomakas, C., von Seelen, W.: Walking Pedestrian Recognition. IEEE Trans. on ITS 1, 155–163 (2000)

    Google Scholar 

  8. Oren, M., Papageorgiu, C., Sihna, P., Osuna, E., Poggio, T.: Pedestrian Detection using Wavelet Templates. In: Procs. IEEE Conf. on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 193–199 (1997)

    Google Scholar 

  9. Zhao, L., Thorpe, C.: Stereo- and Neural Network-based Pedestrian Detection. In: Procs. IEEE Intl. Conf. on Intelligent Transportation Systems 1999, pp. 298–303 (October 1999)

    Google Scholar 

  10. Heisele, B., Wöhler, C.: Motion-based Recognition of Pedestrians. In: Procs. IEEE Intl Conf. on Pattern Recognition, pp. 1325–1330 (June 1998)

    Google Scholar 

  11. Cutler, R., Davis, L.S.: Robust real-time periodic motion detection, analysis and applications. IEEE Trans. on PAMI 22, 781–796 (2000)

    Google Scholar 

  12. Bertozzi, M., Broggi, A., Graf, T., Grisleri, P., Meinecke, M.: Pedestrian Detection in Infrared Images. In: Procs. IEEE Intelligent Vehicles Symposium 2003 (June 2003) (in press)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università di Parma, Parma, I-43100, Italy

    M. Bertozzi, A. Broggi, M. Carletti, A. Fascioli & P. Grisleri

  2. Volkswagen AG, Electronic Research, Wolfsburg, D-38436, Germany

    T. Graf & M. Meinecke

Authors
  1. M. Bertozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Broggi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Carletti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Fascioli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T. Graf
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. P. Grisleri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. M. Meinecke
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Otto-von-Guericke University, 39106, Magdeburg, Germany

    Bernd Michaelis

  2. Institute for Electronics, Signal Processing and Communications (IESK), Otto-von-Guericke-University Magdeburg, P.O. Box 4120, D-39016, Magdeburg, Germany

    Gerald Krell

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bertozzi, M. et al. (2003). IR Pedestrian Detection for Advanced Driver Assistance Systems. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45243-0_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40861-1

  • Online ISBN: 978-3-540-45243-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation